We learn graph-based similarity measures for the task of extracting word synonyms from a corpus of parsed text. A constrained graph walk variant that has been successfully applied in the past in similar settings is shown to outperform a state-of-the-art syntactic vectorbased approach on this task. Further, we show that learning specialized similarity measures for different word types is advanta...

In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features based on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our mo...

Session-based recommendation plays a central role in wide spectrum of online applications, ranging from e-commerce to advertising services. However, the majority existing session-based techniques (e.g., attention-based recurrent network or graph neural network) are not well-designed for capturing complex transition dynamics exhibited with temporally-ordered and multi-level interdependent relati...

A user-centred design approach in combination with a tool-supported development of abstract prototypes is proposed. Based on task, object and user models an editor for modelling the navigation structure of a user interface as a dialogue graph is demonstrated. This editor is based on the XIML technology and allows simulations considering temporal relations between task and design decisions for t...

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most the current techniques consider each channel as independent, neglecting functional connectivity brain during activity and are primarily subject specific. This paper proposes graph signal representation to classify pair using multi-chan...